Welcome! While we're waiting:

• Please download the workshop files from: https://github.com/dlab-geo/r-leaflet-workshop

• Unzip the Zipfile: https://github.com/dlab-geo/r-leaflet-workshop/archive/master.zip

• Open RStudio

• Open a new R script file

Basic Maps

• Marker Maps
• Adding Popups

Data Maps

• Symbology for mapping point data
• Choropleth maps
• Adding Legends

Doing More

• Customizing the UI
• Sharing your maps

This workshop/tutorial will walk you through the basics of using the Leaflet mapping package in R. You can follow along in any of the three formats:

• Tutorial Page (leaflet-webmaps-in-R.html)
• Raw code (leaflet-webmaps-in-R.R)
• Slides (leaflet-webmaps-in-R-slides.html)

Make sure you can copy and paste from one of the above into the script.

To begin, lets set up our packages and environment.

Load the packages we will use today

library(leaflet)
library(RColorBrewer)
library(sp)
library(rgdal)
library(htmlwidgets)
library(magrittr) # or dplyr

Install any packages that you do not have on your computer

# install.packages("leaflet")
# install.packages("RColorBrewer")
# install.packages("sp")
# install.packages("rgdal")
# install.packages("htmlwidgets")
# install.packages("magrittr") # or dplyr

to the folder in which you unzipped the workshop files

Leaflet is a lightweight, yet powerful javascript library for creating interactive web maps.

Leaflet maps are a combination of HTML and Javascript code that is meant to be rendered in a web browser.

We can use the R leaflet package to create Leaflet maps in R

There are a number of R packages for making Leaflet maps.

• leaflet is actively maintained, highly customizable, and integrates well with other R libraries.

• tmap is easier for getting started but less customizeable
  • also great for interactive data analysis

map1 <- leaflet()       # Initialize the map object
map1 <- addTiles(map1)  # Add basemap tiles
map1                    # Display the map

WARNING don't call your map object map

map1 <- leaflet()       # Initialize the map object
map1 <- addTiles(map1)  # Add basemap tiles - default is OpenStreetMap
map1                    # Display the map

Specify the center and zoom level for the map

map1 <- leaflet() %>%
        addTiles() %>%  
        setView(lat=37.870044, lng=-122.258169, zoom = 15)
map1  

map1  # setView(lat=37.870044, lng=-122.258169, zoom = 15)

Chains commands so that the output of one command becomes the input of the next command.

map2 <- leaflet() %>%
        addTiles() %>%  
        setView(lat=37.870044, lng=-122.258169, zoom = 15)
map2   

Requires dplyr or magrittr package to be loaded

map2

Regular

map1 <- leaflet()        
map1 <- addTiles(map1)   
map1 <- setView(map1, lat=37.870044, lng=-122.258169, zoom = 15)
map1                  

Piping

map2 <- leaflet() %>%
        addTiles() %>%  
        setView(lat=37.870044, lng=-122.258169, zoom = 15)
map2   

Create a leaflet map centered on the Doe Library

• You can get the coordinates by right clicking in Google Maps and selecting what's here

Questions

• What zoom level displays the names of campus buildings?
• What zoom level shows all of Berkeley?
• What is the max/min zoom level that returns a basemap?

By Default, Leaflet uses the OpenStreetMap basemap, which is added with the addTiles() function

leaflet() %>% addTiles() %>% 
           setView(lat=37.870, lng=-122.258, zoom = 15)

Use addProviderTiles with the name of the basemap to add a different basemap.

Create a leaflet map with the ESRI World Street Map basemap.

map2 <- leaflet() %>%
        addProviderTiles("Esri.WorldStreetMap") %>% 
        setView(lat=37.870044, lng=-122.258169, zoom = 12)

map2   #Using ESRI WorldStreetMap basemap

Add a different basemap by taking a look at this web page of available basemaps

http://leaflet-extras.github.io/leaflet-providers/preview/

Use the provider name in quotes to access the basemap.

leaflet() %>% addProviderTiles("CartoDB.Positron") %>% 
    setView(lat=37.870044, lng=-122.258169, zoom = 12)

For more info, read the documentation

?addProviderTiles

Use addMarkers to add one or more data points to the map.

map3 <- leaflet() %>%
  addTiles() %>%
  addMarkers(lat=37.870044, lng=-122.258169, popup="Go Bears!")
map3

map3 <- leaflet() %>%
  addTiles() %>%
  #setView(lat=37.870044, lng=-122.258169, zoom = 17) %>%
  addMarkers(lat=37.870044, lng=-122.258169, popup="Go Bears!")
map3

• The map will automatically center on the marker data and determine the zoom level.

• You don't need to use setView (commented out).

• But, you can override the default center & zoom by using setView.

What does popup= do?

?addMarkers

addMarkers(map, lng = NULL, lat = NULL, layerId = NULL, group = NULL,
  icon = NULL, popup = NULL, popupOptions = NULL, label = NULL,
  labelOptions = NULL, options = markerOptions(), clusterOptions = NULL,
  clusterId = NULL, data = getMapData(map))

How would you add a second marker for Cafe Milano?

• 37.868641, -122.258537

• Try it?

map3 <- leaflet() %>%
  addTiles() %>%
  addMarkers(lat=37.870044, lng=-122.258169, popup="Go Bears!") %>%
  addMarkers(lat=37.868641, lng=-122.258537, popup="Cafe Milano")
map3

map3 <- leaflet() %>%
  addTiles() %>%
  addMarkers(lat = c(37.870044,37.868641), 
             lng = c(-122.258169,-122.258537),
             popup = c("Go Bears", "Cafe Milano"))

map3

Source: Caltrans GIS Data

bart <- read.csv('data/bart.csv', stringsAsFactors = FALSE)  
str(bart)

## 'data.frame':    44 obs. of  6 variables:
##  $ X       : num  -122 -122 -122 -122 -122 ...
##  $ Y       : num  37.9 37.9 37.9 37.8 37.8 ...
##  $ STATION : chr  "NORTH BERKELEY" "DOWNTOWN BERKELEY" "ASHBY" "ROCKRIDGE" ...
##  $ OPERATOR: chr  "BART" "BART" "BART" "BART" ...
##  $ DIST    : int  4 4 4 4 4 4 4 4 4 4 ...
##  $ CO      : chr  "ALA" "ALA" "ALA" "ALA" ...

What column(s) contain the geographic data?

map4 <- leaflet() %>%
         addTiles() %>%   
         addMarkers(lat=bart$Y, lng=bart$X, 
         popup= paste("Station:", bart$STATION))
map4

map4

Point data are often stored in CSV files and loaded into R data frames.

These point data are manipulated like other R data frames.

Leaflet can map these data if the points use geographic coordinates (longitude & latitude).

More complex geographic data are commonly stored in ESRI Shapefiles.

To get the most out of spatial data in R you should use packages specifically for working with spatial data.

We can use the sp and rgdal packages to import, manipulate and map more complex spatial objects.

sp - R classes and methods for spatial data

rgdal - Functions for importing and transforming spatial data

Let's use these to import data in ESRI Shapefiles

BART Lines

Source: Caltrans GIS Data

dir("data/BART_13/")

## [1] "BART_13.dbf"     "BART_13.prj"     "BART_13.sbn"     "BART_13.sbx"    
## [5] "BART_13.shp"     "BART_13.shp.xml" "BART_13.shx"

Use rgdal to read in the data from a Shapefile

library(rgdal)
bart_lines <- readOGR(dsn="data/BART_13",layer="BART_13")

## OGR data source with driver: ESRI Shapefile 
## Source: "data/BART_13", layer: "BART_13"
## with 6 features
## It has 3 fields

Take a look at the data

summary(bart_lines)

## Object of class SpatialLinesDataFrame
## Coordinates:
##          min        max
## x -122.47113 -121.89932
## y   37.55675   38.02386
## Is projected: FALSE 
## proj4string :
## [+proj=longlat +datum=NAD83 +no_defs +ellps=GRS80 +towgs84=0,0,0]
## Data attributes:
##                                     ROUTE     Shape_Leng        LINE  
##  Dublin/Pleasanton to Daly City        :1   Min.   : 3686   Blue  :1  
##  Fremont to Daly City                  :1   1st Qu.:62836   Green :1  
##  Fremont to Richmond                   :1   Median :64169   Orange:1  
##  Millbrae to SFO Shuttle               :1   Mean   :58883   Red   :1  
##  Pittsburg/Bay Point to SFO to Millbrae:1   3rd Qu.:64962   Yellow:1  
##  Richmond to Daly City to Millbrae     :1   Max.   :93658   NA's  :1

Use addPolyLines to add linear features

map4 <- leaflet() %>%
         addTiles() %>%   
         addMarkers(lat=bart$Y, lng=bart$X, 
         popup= paste("Station:", bart$STATION)) %>%   
         addPolylines(data=bart_lines, color="red", weight=3)
map4

Leaflet can map both data frames & spatial objects!

map4

Let's add BART Service Area data from the Metropolitan Transportation Commission (MTC)

dir("data/Transit_Service_Areas_2016")

##  [1] "bart_service_area.dbf"          "bart_service_area.prj"         
##  [3] "bart_service_area.qpj"          "bart_service_area.shp"         
##  [5] "bart_service_area.shx"          "Transit_Service_Areas_2016.cpg"
##  [7] "Transit_Service_Areas_2016.dbf" "Transit_Service_Areas_2016.prj"
##  [9] "Transit_Service_Areas_2016.shp" "Transit_Service_Areas_2016.shx"
## [11] "Transit_Service_Areas_2016.xml"

Let's add BART Service Area data from the Metropolitan Transportation Commission (MTC)

bart_service <- readOGR(dsn="data/Transit_Service_Areas_2016",layer="bart_service_area")

## OGR data source with driver: ESRI Shapefile 
## Source: "data/Transit_Service_Areas_2016", layer: "bart_service_area"
## with 3 features
## It has 7 fields
## Integer64 fields read as strings:  OBJECTID

Take a look at the data

summary(bart_service)

## Object of class SpatialPolygonsDataFrame
## Coordinates:
##          min        max
## x -122.47784 -121.77617
## y   37.32445   38.02598
## Is projected: FALSE 
## proj4string :
## [+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0]
## Data attributes:
##  OBJECTID agency_id agency_nam                status   system 
##  13:1     BA:3      BART:3     Existing          :1   Rail:3  
##  14:1                          Planned           :1           
##  15:1                          Under Construction:1           
##                                                               
##                                                               
##                                                               
##    Shape__Are         Shape__Len    
##  Min.   : 9885713   Min.   : 22981  
##  1st Qu.:10027470   1st Qu.: 24130  
##  Median :10169227   Median : 25279  
##  Mean   :35531127   Mean   : 84825  
##  3rd Qu.:48353833   3rd Qu.:115747  
##  Max.   :86538439   Max.   :206215

Use addPolygons to add area features

map4 <- leaflet() %>%
         addTiles() %>%   
         addMarkers(lat=bart$Y, lng=bart$X, 
         popup= paste("Station:", bart$STATION)) %>%   
         addPolylines(data=bart_lines, color="red", weight=3) %>%
         addPolygons(data=bart_service, color="blue", opacity = 0.6)
map4

map4

Redo the previous map and:

• change the basemap to "CartoDB.Positron"

• set the default view to center on San Francisco

Save the map as an HTML file that you can then open in a browser, share with friends, put online.

#library(htmlwidgets)
saveWidget(map4, file="bartmap.html")

San Francisco Open Data Portal

SF Property Tax Rolls

This data set includes the Office of the Assessor-Recorder’s secured property tax roll spanning from 2015.

We are using this as a proxy for home values.

We are working with a simplified version of the full data set.

Set your working directory first to the folder where you downloaded the workshop files!

sfhomes <- read.csv('data/sfhomes15.csv', stringsAsFactors = FALSE)  
str(sfhomes)

## 'data.frame':    680 obs. of  11 variables:
##  $ SalesDate   : chr  "2015-08-21" "2015-08-13" "2015-12-29" "2015-07-06" ...
##  $ Address     : chr  "0000 2760 19TH                AV0015" "0000 0560AMISSOURI            ST0000" "0000 0718 LONG BRIDGE         ST1202" "0000 0899 VALENCIA            ST0202" ...
##  $ YrBuilt     : int  1979 2003 2016 2015 1961 1900 2015 NA 1947 1907 ...
##  $ NumBeds     : int  2 2 2 3 3 2 2 0 0 3 ...
##  $ NumBaths    : int  2 2 2 3 3 2 2 0 0 3 ...
##  $ NumUnits    : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ AreaSqFt    : int  1595 1191 1346 1266 1840 1256 1520 536 950 1837 ...
##  $ Neighborhood: chr  "Twin Peaks" "Potrero Hill" "Mission Bay" "Mission" ...
##  $ Value       : int  865000 1402560 2260993 1700000 2309692 2700564 1925000 583768 944180 1750001 ...
##  $ lat         : num  37.7 37.8 37.8 37.8 37.8 ...
##  $ lon         : num  -122 -122 -122 -122 -122 ...

head(sfhomes)

##    SalesDate                              Address YrBuilt NumBeds NumBaths
## 1 2015-08-21 0000 2760 19TH                AV0015    1979       2        2
## 2 2015-08-13 0000 0560AMISSOURI            ST0000    2003       2        2
## 3 2015-12-29 0000 0718 LONG BRIDGE         ST1202    2016       2        2
## 4 2015-07-06 0000 0899 VALENCIA            ST0202    2015       3        3
## 5 2015-06-12 0000 1333 JONES               ST0808    1961       3        3
## 6 2015-04-14 0000 1904 BAKER               ST0000    1900       2        2
##   NumUnits AreaSqFt    Neighborhood   Value      lat       lon
## 1        1     1595      Twin Peaks  865000 37.73601 -122.4741
## 2        1     1191    Potrero Hill 1402560 37.75920 -122.3965
## 3        1     1346     Mission Bay 2260993 37.77181 -122.3942
## 4        1     1266         Mission 1700000 37.75876 -122.4210
## 5        1     1840        Nob Hill 2309692 37.79362 -122.4149
## 6        1     1256 Pacific Heights 2700564 37.78881 -122.4437

map4 <- leaflet() %>%
  addTiles() %>%   
  addMarkers(lat=sfhomes$lat, lng=sfhomes$lon, 
            popup= paste("Address:", sfhomes$Address,
                         "<br>", # add line break
                         "Property Value: ", sfhomes$Value))

map4    

We can add to and save the popup code and re-use it instead of typing it over and over again.

popup_content <- paste("<b>Address:</b>", sfhomes$Address,"<br>", 
                       "<b>Property Value</b>: ", sfhomes$Value, "<br>",
                       "<b>Neighborhood:</b> ", sfhomes$Neighborhood, "<br>",
                       "<b>Num Bedrooms: </b>", sfhomes$NumBeds, "<br>",
                       "<b>Num Bathrooms:</b>", sfhomes$NumBaths
                       )

map4 <- leaflet() %>%
          addTiles() %>%   
          addMarkers(lat=sfhomes$lat, lng=sfhomes$lon, 
                     popup= popup_content)

map4

Instead of this:

leaflet() %>%  
  addTiles() %>%   
  addMarkers(lat=sfhomes$lat, lng=sfhomes$lon, popup= popup_content)

We can use this syntax:

leaflet(sfhomes) %>%
  addTiles() %>%   
  addMarkers(~lon, ~lat, popup = popup_content)

When the addMarkers function arguments lng= and lat= are not named they must be in the expected order (longitude, latitude)!

Read the addMarker documentation for options to address this.

addMarkers(map, lng = NULL, lat = NULL, layerId = NULL, 
           group = NULL, icon = NULL, popup = NULL, 
           options = markerOptions(), 
           clusterOptions = NULL, clusterId = NULL, 
           data = getMapData(map))

map4 <- leaflet(sfhomes) %>%
  addTiles() %>%   
  addMarkers(~lon, ~lat, popup= popup_content,
            clusterOptions = 1)

map4  # Explore the Map - hover over a cluster marker, zoom in.

addCircleMarker

map4 <- leaflet(sfhomes) %>%
  addTiles() %>%   
  addCircleMarkers(~lon, ~lat, popup = popup_content)

addCircleMarker

map4 

addCircleMarkers(map, lng = NULL, lat = NULL, radius = 10, 
    layerId = NULL, group = NULL, stroke = TRUE, color = "#03F", 
    weight = 5, opacity = 0.5, 
    fill = TRUE, fillColor = color, ....)

Change color, radius and stroke weight of circle markers

map4 <- leaflet(sfhomes) %>%
  addTiles() %>%   
  addCircleMarkers(~lon, ~lat, popup = popup_content,
             color="white", radius=6, weight=2,   # stroke
             fillColor="red",fillOpacity = 0.75   # fill
             )

• Use colors() to see a list of all R named colors.
• You can expand on these with hexidecimal color values.

map4 

Can you cluster circleMarkers?

Use data values to determine the color and size of symbols.

We can symbolize the size of points by data values by making the radius of the circle a function of a data value.

map4 <- leaflet(sfhomes) %>%
  addProviderTiles("CartoDB.Positron") %>%
  addCircleMarkers(~lon, ~lat, popup=popup_content,
             fillColor= NA, color="Red", weight=1, fillOpacity = 0,
             radius= ~NumBeds+2
             )

map4  # Size is a function of what variable?

Circles and CircleMarkers look quite similar.

Circle radii are specified in meters while CircleMarkers are specified in pixels.

map4b <- leaflet(sfhomes) %>%
  addProviderTiles("CartoDB.Positron") %>%
  addCircles(~lon, ~lat, popup=popup_content,
             fillColor= NA, color="Red", 
             weight=1, fillOpacity = 0,
             radius= ~NumBeds*10
             )

Circle radii are specified in meters not pixels - Zoom in to see how it works.

map4b  # Compare map4 and map4b at different zoom levels

The RColorBrewer package is widely used to create color palettes for maps.

• A color palette is a set of colors

There are 3 types of color palettes

1. Qualitative Color Palettes
2. Sequential Color Palettes
3. Diverging Color Palettes

Contrasting colors for categorical data

display.brewer.all(type="qual") 

display.brewer.pal(7, "Set3" )  # Try a different number of colors

For highlighting trends in numerical data

display.brewer.all(type="seq")

For highlighting the outliers

display.brewer.all(type="div")

Let's map sfhomes by the values in the Neighborhood column.

First, check out the RColorBrewer qualitative color palettes

display.brewer.all(type="qual")

colorfactor takes as input a color palette and a domain that contains the full range of possible values to be mapped.

colorfactor returns a function specific to that domain that can be used to output a set of color values.

# Create a qualitative color palette
myColor_function <- colorFactor("Paired", sfhomes$Neighborhood) 

map4 <- leaflet(sfhomes) %>%
  addProviderTiles("CartoDB.Positron") %>%
  addCircleMarkers(~lon, ~lat,  
             popup= popup_content,
             fillColor= ~myColor_function(Neighborhood),
             radius=6, color=NA, weight=2, fillOpacity = 1
             )

map4  # what neighborhood had the most 2015 transactions?

map4 <- leaflet(sfhomes) %>%
  addProviderTiles("CartoDB.Positron") %>%
  addCircleMarkers(~lon, ~lat, popup=popup_content,
             fillColor= ~myColor_function(Neighborhood),
             radius=6, color=NA, weight=2,fillOpacity = 1
             ) %>%
      addLegend(title = "Neighborhood", pal =  myColor_function,
                values = ~Neighborhood, opacity = 1, 
                position="bottomleft")

map4 

For mapping colors to qualitative data

?colorfactor

colorFactor(palette, domain, levels = NULL, ordered = FALSE,
  na.color = "#808080", alpha = FALSE, reverse = FALSE)

Let's map the homes by value.

First, check out the sequential color palettes

display.brewer.all(type="seq")

For simple linear scaling of colors to values use colorNumeric.

First, create the color mapping function

numColor_function <- colorNumeric("Reds", sfhomes$Value)

Use the numColor_function to create a proportional color map

map4 <- leaflet(sfhomes) %>%
  addProviderTiles("CartoDB.Positron") %>%
  addCircleMarkers(~lon, ~lat, popup=popup_content,
             fillColor= ~numColor_function(Value),
             radius=6, color="grey", weight=1, fillOpacity = 1
             ) %>%
      addLegend(title = "Property Values", pal =  numColor_function,
                values = ~Value, opacity = 1, 
                position="bottomleft")

map4  # proportional color map

For simple linear scaling of colors to values use colorNumeric.

?colorNumeric

colorNumeric(palette, domain, na.color = "#808080", alpha = FALSE,
  reverse = FALSE)

Use colorQuantile to create a color palette based on quantile binning of the data.

First, create the color mapping function

quantColor_function <- colorQuantile("Reds", sfhomes$Value, n=5)

Use the color function to map colors to house values

map4b <- leaflet(sfhomes) %>%
  addProviderTiles("CartoDB.Positron") %>%
  addCircleMarkers(~lon, ~lat, popup=popup_content,
             fillColor= ~quantColor_function(Value),
             radius=6, color="grey", weight=1,fillOpacity = 1
             ) 

map4b  # Graduated color map

For mapping colors to data binned by quantiles

?colorQuantile

colorQuantile(palette, domain, n = 4,
  probs = seq(0, 1, length.out = n + 1),
  na.color = "#808080", alpha = FALSE, reverse = FALSE)

For more control over customizing colors see the colorBin function which can be used to create color palettes based on different classification methods for binning the data, eg equal interval, natural breaks etc.

?colorBin

map4b %>%  addLegend(title = "Value", pal =  quantColor_function,
                values = ~Value, opacity = 1, 
                position="bottomleft")

map5 <-map4b %>%   addLegend(pal = quantColor_function, values = ~Value,
                     title = "Property Value, 2015",
                     position="bottomleft",
                     opacity=1,
                     labFormat = function(type, cuts, p) {
                      n = length(cuts)
                      cuts = paste0("$", format(cuts[-n], big.mark=","), 
                              " - ", "$",format(cuts[-1], big.mark=","))
                      }
                   )

map5  # Graduated Color Map

Basic Maps

• addMarkers - Simple Marker Maps
• addCircleMarkers - Circle Marker Maps

Data Maps

• addCircles Proportional symbol maps
• colorFactor - Category Maps
• colorNumeric - Proportional color maps
• colorQuantile - Graduated color maps

Color areas based on data values.

The data values are classified into bins.

Quantile classification is the default.

Each bin gets a unique color from a color palette.

sf_md_hhi <- readOGR(dsn="data",layer="sf_medhhincome_acs5y_16")

## OGR data source with driver: ESRI Shapefile 
## Source: "data", layer: "sf_medhhincome_acs5y_16"
## with 196 features
## It has 5 fields

summary(sf_md_hhi)

## Object of class SpatialPolygonsDataFrame
## Coordinates:
##          min        max
## x -123.01392 -122.32756
## y   37.69274   37.86334
## Is projected: FALSE 
## proj4string :
## [+proj=longlat +datum=NAD83 +no_defs +ellps=GRS80 +towgs84=0,0,0]
## Data attributes:
##          GEOID    
##  06075010100:  1  
##  06075010200:  1  
##  06075010300:  1  
##  06075010400:  1  
##  06075010500:  1  
##  06075010600:  1  
##  (Other)    :190  
##                                                  NAME           variable  
##  Census Tract 101, San Francisco County, California:  1   B19013_001:196  
##  Census Tract 102, San Francisco County, California:  1                   
##  Census Tract 103, San Francisco County, California:  1                   
##  Census Tract 104, San Francisco County, California:  1                   
##  Census Tract 105, San Francisco County, California:  1                   
##  Census Tract 106, San Francisco County, California:  1                   
##  (Other)                                           :190                   
##     estimate           moe       
##  Min.   : 11971   Min.   :  739  
##  1st Qu.: 65893   1st Qu.: 9744  
##  Median : 89668   Median :15343  
##  Mean   : 91151   Mean   :16211  
##  3rd Qu.:117922   3rd Qu.:20032  
##  Max.   :205865   Max.   :89454  
##  NA's   :2        NA's   :2

Map sf_md_hhi with addPolygons

map6 <- leaflet() %>%
  addTiles() %>%
  addPolygons(data=sf_md_hhi)

map6 # using addPolygons to map sf_md_hhi

?addPolygons

addPolygons(map, lng = NULL, lat = NULL, layerId = NULL, group = NULL,
            stroke = TRUE, color = "#03F", weight = 5, opacity = 0.5,
            fill = TRUE, fillColor = color, fillOpacity = 0.2, 
            dashArray = NULL, smoothFactor = 1, noClip = FALSE, 
            popup = NULL, popupOptions = NULL, label = NULL, 
            labelOptions = NULL, options = pathOptions(),
            highlightOptions = NULL, data = getMapData(map))

map6 <- leaflet() %>%
  setView(lng=-122.448889, lat=37.764645, zoom=12) %>%
  addProviderTiles("CartoDB.Positron") %>%
  
  # Customize the symbology of the polygons
  addPolygons(data=sf_md_hhi, color="grey", weight=1,
              fillColor="Orange", fillOpacity = 0.25)

map6  # color="grey", weight=1, fillColor="Orange", fillOpacity = 0.25

Median Household Income is in the estimate column

Recipe:

1. Create a color function based on the values of estimate
2. Map the polygons setting the color to values produced by the color function

• First, select the name of the color palette

#display.brewer.all(type="seq") 

Then create the color mapping function

##
quantColor_function <- colorQuantile("YlOrRd", sf_md_hhi$estimate, n=5)

A choropleth map is a graduated color map of polygon data.

map6 <- leaflet() %>%
  setView(lng=-122.448889, lat=37.764645, zoom=12) %>%
  addProviderTiles("CartoDB.Positron") %>%
  # 
  addPolygons(data=sf_md_hhi, 
              color="white", 
              weight=1, 
              opacity=0.5,
              fillColor=~quantColor_function(estimate), 
              fillOpacity = 0.65,
              popup = paste0("$",sf_md_hhi$estimate))

map6  # choropleth map of median household income by census tract

map6 <- map6 %>% addLegend(pal = quantColor_function, 
                   values = sf_md_hhi$estimate,
                   title = "Median HH Income",
                   position="bottomleft",
                   opacity=1,
                   labFormat = function(type, cuts, p) {
                     n = length(cuts)
                     cuts = paste0("$", format(cuts[-n], big.mark=","), 
                             " - ", "$",format(cuts[-1], big.mark=","))
                   }
)

map6

You can add multiple data layers to a leaflet map.

Let's add the sfhomes to the map

cheap <- sfhomes[sfhomes$Value < 1000000,]

map7 <- leaflet() %>%
  setView(lng=-122.448889, lat=37.764645, zoom=12) %>%
  addProviderTiles("CartoDB.Positron") %>%
  
  # Median household income polygons
  addPolygons(data=sf_md_hhi, color="white", weight=1, opacity=0.5,
              fillColor=~quantColor_function(estimate), fillOpacity = 0.65,
              popup = paste0("$",sf_md_hhi$estimate)) %>%
  
  # sfhomes points
  addCircleMarkers(data=cheap, popup=paste0("$",cheap$Value),
              color="black",weight=1, radius=6, 
              fillColor="white", fillOpacity = 0.75)

map7  # sfhomes and median household income

We can add a layer switcher control with addLayersControl().

This allows us to toggle on/off the display of a map layer.

First, we need to assign a group to each map layer

?addLayersControl

map8 <- leaflet() %>%
          setView(lng=-122.448889, lat=37.764645, zoom=12) %>%
          addProviderTiles("CartoDB.Positron") %>%
          addPolygons(data=sf_md_hhi, color="white", weight=1, opacity=0.5,
              fillColor=~quantColor_function(estimate), fillOpacity = 0.65,
              popup = paste0("$",sf_md_hhi$estimate),
              group="Median HH Income"
          ) %>%
          addCircleMarkers(data=cheap, popup=paste0("$",cheap$Value),
              color="black",weight=1, radius=6, 
              fillColor="white", fillOpacity = 0.75,
              group="Property Values"
          ) %>%
          addLayersControl(
            overlayGroups = c("Property Values","Median HH Income"),
            options = layersControlOptions(collapsed = FALSE)
        )

map8

map8 <- leaflet() %>%
          setView(lng=-122.448889, lat=37.764645, zoom=12) %>%
          addProviderTiles("CartoDB.Positron", group="Simple Basemap") %>%
          addProviderTiles("Esri.WorldStreetMap", group="Streets Basemap")  %>%
          addTiles("", group="No Basemap") %>%  
          #
          addPolygons(data=sf_md_hhi, color="white", weight=1, opacity=0.5,
              fillColor=~quantColor_function(estimate), fillOpacity = 0.65,
              popup = paste0("$",sf_md_hhi$estimate),
              group="Median HH Income"
          ) %>%
          addCircleMarkers(data=cheap, popup=paste0("$",cheap$Value),
              color="black",weight=1, radius=6, 
              fillColor="white", fillOpacity = 0.75,
              group="Property Values"
          ) %>%
          addLayersControl(
            baseGroups = c("Simple Basemap", "Streets Basemap", "No Basemap"),
            overlayGroups = c("Property Values","Median HH Income"),
            options = layersControlOptions(collapsed = FALSE)
        )

map8

Interactive Maps in R - check

Web Maps can be shared if online.

Easy way to go is RPubs

You can share you map online by publishing it to RPubs.

• You need to have an RPubs account to make that work.

1. Enter the code for your map in the console

2. In the Viewer window, click on the Publish icon.

DEMO… http://rpubs.com/pfrontiera/368070

Another way to share your map is to save it to a file. You can then email it, host it on your own web server or host it on github, etc.

#library(htmlwidgets)
saveWidget(map7, file="testmap.html")

Open your file to by double-clicking on it in the Mac Finder or Windows Explorer.

Flexdashboard

Shiny

Crosstalk

Flexdashboard is a tool for creating interactive dashboards with R Markdown.

Flexdashboard Demo

A framework for creating interactive web apps based on R code.

Shiny Demo

Shiny User Gallery

Shiny online tutorial

http://www.shinyapps.io - A freemium web platform for hosting your shiny apps.

An R package for creating interactive web documents without needing a server.

https://rstudio.github.io/crosstalk

More functionality than Flexdashboard, less than Shiny

Getting more Practice

Leaflet for R Tutorial

To you!

and to Josh Pepper who did an earlier workshop on which these materials are loosely based.

You can add an online, georectified scanned map to leaflet.

There are many of these online at the New York Public Library or MapWarper, and other websites.

Let's add this map of Berkeley in 1880

Here we are combining addTiles and addProviderTiles

mapurl <- "https://mapwarper.net/maps/tile/25477/{z}/{x}/{y}.png"

map2 <- leaflet() %>%
  addProviderTiles("CartoDB.Positron") %>%
  addTiles(mapurl) %>%  # custom map image
  setView(lat=37.870044, lng=-122.258169, zoom = 13)

map2  # Map of Berkeley, 1880 overlaid on the CartoDB basemap